An SVM Based Approach for Cardiac View Planning

TL;DR

This paper presents an automated SVM-based method for prescribing cardiac MRI planes, improving accuracy and efficiency by using anatomical features and genetic algorithms for optimization.

Contribution

The study introduces a novel automated approach combining support vector regression and genetic algorithms for cardiac plane planning in MRI.

Findings

Achieved less than 15-degree deviation in 90% of cases

Demonstrated robustness across varying SNR levels

Validated with 6-fold cross validation

Abstract

We consider the problem of automatically prescribing oblique planes (short axis, 4 chamber and 2 chamber views) in Cardiac Magnetic Resonance Imaging (MRI). A concern with technologist-driven acquisitions of these planes is the quality and time taken for the total examination. We propose an automated solution incorporating anatomical features external to the cardiac region. The solution uses support vector machine regression models wherein complexity and feature selection are optimized using multi-objective genetic algorithms. Additionally, we examine the robustness of our approach by training our models on images with additive Rician-Gaussian mixtures at varying Signal to Noise (SNR) levels. Our approach has shown promising results, with an angular deviation of less than 15 degrees on 90% cases across oblique planes, measured in terms of average 6-fold cross validation performance -- this is generally within acceptable bounds of variation as specified by clinicians.